Developing and Implementing a Constellation Mentoring Program with Canadian Varsity Soccer Teams
Bibliographic record
Abstract
Peer mentors can assist first-year student-athletes transitional demands by offering perspectives and resources to support their adjustment processes. Sports psychology practitioners (SPPs) can form mentoring programs to foster supportive relationships between athletes. We authored this manuscript to showcase the development of a constellation mentoring program (CMP), a form of mentoring where student-athletes had multiple mentors, integrated within two Canadian varsity soccer (football) sport contexts. We begin by situating our university context and why the CMP, underpinned by the athletic career transition model and constellation theory, was needed to support first-year student-athletes’ transitions. We then introduce the stages of the program, beginning with its development, spanning its implementation, and ending with an evaluation to gauge its effectiveness. Each stage was informed using student-athletes’ experiences of their demands in their university contexts. We conclude with reflections on mentor training, preparatory planning, team-building activities, and making time for reflective practice to help guide SPPs interested in adapting this program for use within further contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".